Análisis de ingredientes en recetas

Análisis de https://www.nature.com/articles/srep00196.pdf

Podemos usar read_lines_chunked si el archivo original es grande. En este ejemplo, filtramos las recetas East Asian:

library(tidyverse)

limpiar <- function(lineas,...){
  str_split(lineas, ',') %>% 
    keep(~.x[1] == 'EastAsian') %>%
    map(~.x[-1]) %>% # quitar tipo de cocina
    map(~.x[nchar(.x) > 0]) # quitar elementos vacios
}
callback_limpiar <- ListCallback$new(limpiar)
filtrado <- read_lines_chunked('../datos/recetas/srep00196-s3.csv',
                    skip = 1, callback = callback_limpiar, chunk_size = 1000)

|==========                                                                      |  13%
|============                                                                    |  15%
|=============                                                                   |  16%
|=============                                                           |  18%    1 MB
|==============                                                          |  20%    1 MB
|================                                                        |  22%    1 MB
|=================                                                       |  24%    1 MB
|==================                                                      |  25%    1 MB
|====================                                                    |  27%    1 MB
|=====================                                                   |  29%    1 MB
|======================                                                  |  31%    1 MB
|=======================                                                 |  32%    1 MB
|=========================                                               |  34%    1 MB
|==========================                                              |  36%    2 MB
|===========================                                             |  37%    2 MB
|============================                                            |  39%    2 MB
|==============================                                          |  41%    2 MB
|===============================                                         |  43%    2 MB
|================================                                        |  44%    2 MB
|=================================                                       |  46%    2 MB
|===================================                                     |  48%    2 MB
|====================================                                    |  49%    2 MB
|=====================================                                   |  51%    2 MB
|======================================                                  |  53%    2 MB
|========================================                                |  55%    3 MB
|=========================================                               |  56%    3 MB
|==========================================                              |  58%    3 MB
|===========================================                             |  60%    3 MB
|=============================================                           |  61%    3 MB
|==============================================                          |  63%    3 MB
|===============================================                         |  65%    3 MB
|================================================                        |  67%    3 MB
|==================================================                      |  68%    3 MB
|===================================================                     |  70%    3 MB
|====================================================                    |  72%    4 MB
|=====================================================                   |  73%    4 MB
|=======================================================                 |  75%    4 MB
|========================================================                |  77%    4 MB
|=========================================================               |  78%    4 MB
|==========================================================              |  80%    4 MB
|============================================================            |  82%    4 MB
|=============================================================           |  84%    4 MB
|==============================================================          |  85%    4 MB
|================================================================        |  87%    4 MB
|=================================================================       |  89%    5 MB
|==================================================================      |  91%    5 MB
|====================================================================    |  93%    5 MB
|=====================================================================   |  95%    5 MB
|======================================================================= |  97%    5 MB
|========================================================================|  99%    5 MB
|=========================================================================| 100%    5 MB

recetas <-  filtrado %>% flatten

recetas[1:10]

[[1]]
[1] "beef_broth" "egg"        "soy_sauce"  "soybean"   

[[2]]
[1] "sesame_oil"          "beef"                "roasted_sesame_seed"
[4] "matsutake"           "black_pepper"        "scallion"           
[7] "garlic"              "soy_sauce"          

[[3]]
[1] "vinegar"             "roasted_sesame_seed" "cayenne"            
[4] "scallion"            "garlic"              "soybean"            
[7] "cucumber"            "rice"               

[[4]]
 [1] "beef"                "roasted_sesame_seed" "soy_sauce"          
 [4] "cayenne"             "ginger"              "scallion"           
 [7] "lettuce"             "garlic"              "vegetable"          
[10] "sake"               

[[5]]
[1] "garlic"    "fish"      "cayenne"   "soy_sauce" "potato"   

[[6]]
 [1] "sweet_potato"        "onion"               "roasted_sesame_seed"
 [4] "soy_sauce"           "cayenne"             "ginger"             
 [7] "soybean"             "vegetable"           "cabbage"            
[10] "rice"                "chicken"             "sesame_oil"         

[[7]]
 [1] "sesame_oil"          "radish"              "fish"               
 [4] "black_pepper"        "ginger"              "garlic"             
 [7] "seaweed"             "shrimp"              "beef"               
[10] "roasted_sesame_seed" "soy_sauce"           "cayenne"            
[13] "chinese_cabbage"     "scallion"            "sesame_seed"        
[16] "rice"               

[[8]]
 [1] "vinegar"   "radish"    "fish"      "cayenne"   "scallion"  "cucumber"  "soybean"  
 [8] "vegetable" "garlic"    "rice"      "soy_sauce"

[[9]]
[1] "radish"        "fish"          "cayenne"       "ginger"        "scallion"     
[6] "garlic"        "vegetable_oil" "soy_sauce"    

[[10]]
[1] "nut"         "cucumber"    "sesame_seed" "soybean"    

library(arules)
length(recetas)

[1] 2512

## No hacer mucho más chico que este soporte, pues tenemos relativamente
## pocas transacciones:
pars <- list(support = 0.05,  target = 'frequent itemsets',
             ext = TRUE)
ap_recetas <- apriori(recetas, parameter = pars)

Apriori

Parameter specification:

Algorithmic control:

Absolute minimum support count: 125 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[242 item(s), 2512 transaction(s)] done [0.00s].
sorting and recoding items ... [41 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 6 done [0.00s].
sorting transactions ... done [0.00s].
writing ... [628 set(s)] done [0.00s].
creating S4 object  ... done [0.00s].

length(ap_recetas)

[1] 628

Vemos los items frecuentes

frecs <- ap_recetas %>% subset(size(.) == 1 ) %>% sort(by = 'support') %>%
 DATAFRAME
DT::datatable(frecs %>% mutate_if(is.numeric, function(x) round(x, 3)))

Y ahora examinamos combinaciones frecuentes de distintos tamaños

ap_recetas %>% 
  subset(size(.) == 2) %>%
  subset(support > 0.20) %>%
  sort(by = 'support') %>%
  inspect

Incluso hay algunas combinaciones de 4 ingredientes que ocurren con frecuencia alta: estos ingredientes son bases de salsas, combinaciones de condimentos:

ap_recetas %>% 
  subset(size(.) == 4) %>%
  subset(support > 0.10) %>%
  sort(by = 'support') %>%
  inspect

Extracción de reglas

pars <- list(support = 0.01, confidence = 0.10,
             target = 'rules',
             ext = TRUE)
reglas_recetas <- apriori(recetas, parameter = pars)

Apriori

Parameter specification:

Algorithmic control:

Absolute minimum support count: 25 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[242 item(s), 2512 transaction(s)] done [0.00s].
sorting and recoding items ... [88 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 6 7 8 done [0.02s].
writing ... [50181 rule(s)] done [0.02s].
creating S4 object  ... done [0.01s].

agregar_hyperlift <- function(reglas, trans){
  quality(reglas) <- cbind(quality(reglas), 
    hyper_lift = interestMeasure(reglas, measure = "hyperLift", 
    transactions = trans))
  reglas
}
reglas_recetas <- agregar_hyperlift(reglas_recetas, recetas)

Análisis de pares comunes

library(arulesViz)

Loading required package: grid
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     

reglas_1 <- subset(reglas_recetas, hyper_lift > 1.1 & support > 0.1 & confidence > 0.40)
length(reglas_1)

[1] 213

reglas_tam_2 <- subset(reglas_1, size(reglas_1)==2)
#inspect(reglas_tam_2 %>% sort(by = 'hyper_lift')) 
plot(reglas_1 %>% subset(support > 0.2), engine = "plotly")

To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.

library(tidygraph)
library(ggraph)
frecs <- 
df_reglas <- reglas_tam_2 %>% DATAFRAME %>% rename(from=LHS, to=RHS) %>% data.frame
df_reglas$weight <- log(df_reglas$lift)
graph_1 <- as_tbl_graph(df_reglas) %>%
  mutate(centrality = centrality_degree(mode = "all")) 
set.seed(881)
ggraph(graph_1, layout = 'fr') +
  geom_edge_link(aes(alpha=lift), 
                 colour = 'red',
                 arrow = arrow(length = unit(4, 'mm'))) + 
  geom_node_point(aes(size = centrality, colour = centrality)) + 
  geom_node_text(aes(label = name), size=4,
                 colour = 'gray20', repel=TRUE) +
  theme_graph(base_family = "sans")

reglas_1 <- subset(reglas_recetas, hyper_lift > 1.5 & confidence > 0.1)
length(reglas_1)

[1] 11068

reglas_tam_2 <- subset(reglas_1, size(reglas_1)==2)
length(reglas_tam_2)

[1] 132

library(tidygraph)
library(ggraph)
df_reglas <- reglas_tam_2 %>% DATAFRAME %>% rename(from=LHS, to=RHS) %>% as_data_frame

`as_data_frame()` is deprecated as of tibble 2.0.0.
Please use `as_tibble()` instead.
The signature and semantics have changed, see `?as_tibble`.
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.

df_reglas$weight <- log(df_reglas$hyper_lift)
graph_1 <- as_tbl_graph(df_reglas) %>%
  mutate(centrality = centrality_degree(mode = "all")) 

ggraph(graph_1, layout = 'fr', start.temp=100) +
  geom_edge_link(aes(alpha=lift), 
                 colour = 'red',
                 arrow = arrow(length = unit(4, 'mm'))) + 
  geom_node_point(aes(size = centrality, colour = centrality)) + 
  geom_node_text(aes(label = name), size=4,
                 colour = 'gray20', repel=TRUE) +
  theme_graph(base_family = "sans")

Exportamos para examinar en Gephi:

write_csv(df_reglas %>% rename(source=from, target=to) %>%
            select(-count), 
          path='reglas.csv')

Nota

La combinación corn y starch puede deberse en parte a una separación incorrecta en el procesamiento de los datos (corn starch o maizena convertido en dos ingredientes, corn y starch):

```r
df_reglas %>% filter(from == \{corn}\, to == \{starch}\)

<!-- rnb-source-end -->

<!-- rnb-frame-begin eyJtZXRhZGF0YSI6eyJjbGFzc2VzIjpbInRibF9kZiIsInRibCIsImRhdGEuZnJhbWUiXSwibnJvdyI6MSwibmNvbCI6OX0sInJkZiI6IlVrUllNd3BZQ2dBQUFBTUFCQUFEQUFNRkFBQUFBQVZWVkVZdE9BQUFCQUlBQUFBQkFBUUFDUUFBQUFGNEFBQURFd0FBQUFrQUFBTU5BQUFBQVFBQUFBc0FBQVFDQUFBQUFRQUVBQWtBQUFBR2JHVjJaV3h6QUFBQUVBQUFBRElBQkFBSkFBQUFDbnRpY21GemMybGpZWDBBQkFBSkFBQUFDSHRpWVhKc1pYbDlBQVFBQ1FBQUFBMTdhMkYwYzNWdlluVnphR2w5QUFRQUNRQUFBQWQ3WW5KbFlXUjlBQVFBQ1FBQUFBWjdiV2xzYTMwQUJBQUpBQUFBQ0h0M1lYTmhZbWw5QUFRQUNRQUFBQWw3YzJWaGQyVmxaSDBBQkFBSkFBQUFCM3Q1WldGemRIMEFCQUFKQUFBQUNIdHphR1Z5Y25sOUFBUUFDUUFBQUF4N1kyVnNaWEo1WDI5cGJIMEFCQUFKQUFBQUJudGpiM0p1ZlFBRUFBa0FBQUFJZTNSdmJXRjBiMzBBQkFBSkFBQUFDWHR0ZFhOMFlYSmtmUUFFQUFrQUFBQUtlMk4xWTNWdFltVnlmUUFFQUFrQUFBQUdlM0JsWVhKOUFBUUFDUUFBQUFWN2JuVjBmUUFFQUFrQUFBQUllM0poWkdsemFIMEFCQUFKQUFBQUQzdGphR2xqYTJWdVgySnliM1JvZlFBRUFBa0FBQUFHZTNkcGJtVjlBQVFBQ1FBQUFBbDdZMmhwWTJ0bGJuMEFCQUFKQUFBQUNIdHpkR0Z5WTJoOUFBUUFDUUFBQUFaN1ltVmhibjBBQkFBSkFBQUFDWHQyYVc1bFoyRnlmUUFFQUFrQUFBQUxlMlZ1YjJ0cFpHRnJaWDBBQkFBSkFBQUFCbnRyWld4d2ZRQUVBQWtBQUFBS2UyMTFjMmh5YjI5dGZRQUVBQWtBQUFBS2UzTm9hV2wwWVd0bGZRQUVBQWtBQUFBSGUyOXVhVzl1ZlFBRUFBa0FBQUFKZTJOaFltSmhaMlY5QUFRQUNRQUFBQVo3Y0c5eWEzMEFCQUFKQUFBQUJudGpiR0Z0ZlFBRUFBa0FBQUFIZTNOeGRXbGtmUUFFQUFrQUFBQUllM05vY21sdGNIMEFCQUFKQUFBQUNIdHdaWEJ3WlhKOUFBUUFDUUFBQUFoN2IzbHpkR1Z5ZlFBRUFBa0FBQUFHZTNOaGEyVjlBQVFBQ1FBQUFBWjdabWx6YUgwQUJBQUpBQUFBREh0aVpXVm1YMkp5YjNSb2ZRQUVBQWtBQUFBTmUySmxiR3hmY0dWd2NHVnlmUUFFQUFrQUFBQVJlMk5vYVc1bGMyVmZZMkZpWW1GblpYMEFCQUFKQUFBQUMzdDJaV2RsZEdGaWJHVjlBQVFBQ1FBQUFBaDdaMmx1WjJWeWZRQUVBQWtBQUFBSmUyTmhlV1Z1Ym1WOUFBUUFDUUFBQUFoN1kyRnljbTkwZlFBRUFBa0FBQUFHZTJKbFpXWjlBQVFBQ1FBQUFBZDdkMmhsWVhSOUFBUUFDUUFBQUFWN1pXZG5mUUFFQUFrQUFBQVZlM0p2WVhOMFpXUmZjMlZ6WVcxbFgzTmxaV1I5QUFRQUNRQUFBQXg3YzJWellXMWxYMjlwYkgwQUJBQUpBQUFBRG50aWJHRmphMTl3WlhCd1pYSjlBQUFFQWdBQUFBRUFCQUFKQUFBQUJXTnNZWE56QUFBQUVBQUFBQUVBQkFBSkFBQUFCbVpoWTNSdmNnQUFBUDRBQUFNTkFBQUFBUUFBQUJnQUFBUUNBQUFDL3dBQUFCQUFBQUF0QUFRQUNRQUFBQWw3WTJGNVpXNXVaWDBBQkFBSkFBQUFDWHR6YjNsaVpXRnVmUUFFQUFrQUFBQUdlM0pwWTJWOUFBUUFDUUFBQUFaN2MyRnJaWDBBQkFBSkFBQUFCWHRsWjJkOUFBUUFDUUFBQUFsN2MyVmhkMlZsWkgwQUJBQUpBQUFBQ0h0M1lYTmhZbWw5QUFRQUNRQUFBQWw3ZG1sdVpXZGhjbjBBQkFBSkFBQUFCM3R2Ym1sdmJuMEFCQUFKQUFBQUNIdG5hVzVuWlhKOUFBUUFDUUFBQUFaN1kyOXlibjBBQkFBSkFBQUFESHRqWld4bGNubGZiMmxzZlFBRUFBa0FBQUFJZTNSdmJXRjBiMzBBQkFBSkFBQUFDbnRqZFdOMWJXSmxjbjBBQkFBSkFBQUFDWHR0ZFhOMFlYSmtmUUFFQUFrQUFBQUllMk5oY25KdmRIMEFCQUFKQUFBQUJYdHVkWFI5QUFRQUNRQUFBQVo3Y0dWaGNuMEFCQUFKQUFBQUNIdHlZV1JwYzJoOUFBUUFDUUFBQUFaN1ltVmxabjBBQkFBSkFBQUFCbnQzYVc1bGZRQUVBQWtBQUFBUGUyTm9hV05yWlc1ZlluSnZkR2g5QUFRQUNRQUFBQWw3WTJocFkydGxibjBBQkFBSkFBQUFDSHR6ZEdGeVkyaDlBQVFBQ1FBQUFBWjdZbVZoYm4wQUJBQUpBQUFBQm50clpXeHdmUUFFQUFrQUFBQUxlMlZ1YjJ0cFpHRnJaWDBBQkFBSkFBQUFDbnR0ZFhOb2NtOXZiWDBBQkFBSkFBQUFDbnR6YUdscGRHRnJaWDBBQkFBSkFBQUFCbnR3YjNKcmZRQUVBQWtBQUFBSmUyTmhZbUpoWjJWOUFBUUFDUUFBQUFkN2MzRjFhV1I5QUFRQUNRQUFBQVo3WTJ4aGJYMEFCQUFKQUFBQUNIdHphSEpwYlhCOUFBUUFDUUFBQUFoN2NHVndjR1Z5ZlFBRUFBa0FBQUFJZTI5NWMzUmxjbjBBQkFBSkFBQUFCbnRtYVhOb2ZRQUVBQWtBQUFBTmUySmxiR3hmY0dWd2NHVnlmUUFFQUFrQUFBQU1lMkpsWldaZlluSnZkR2g5QUFRQUNRQUFBQkY3WTJocGJtVnpaVjlqWVdKaVlXZGxmUUFFQUFrQUFBQUxlM1psWjJWMFlXSnNaWDBBQkFBSkFBQUFCM3QzYUdWaGRIMEFCQUFKQUFBQURIdHpaWE5oYldWZmIybHNmUUFFQUFrQUFBQVZlM0p2WVhOMFpXUmZjMlZ6WVcxbFgzTmxaV1I5QUFRQUNRQUFBQTU3WW14aFkydGZjR1Z3Y0dWeWZRQUFCQUlBQUFQL0FBQUFFQUFBQUFFQUJBQUpBQUFBQm1aaFkzUnZjZ0FBQVA0QUFBQU9BQUFBQVQrTldhNTRxWlJpQUFBQURnQUFBQUUvMmk2TG91aTZMd0FBQUE0QUFBQUJQNkh2c2J1RUU1RUFBQUFPQUFBQUFVQUh3cWduMGJrK0FBQUFEUUFBQUFFQUFBQWtBQUFBRGdBQUFBRS8vTXpNek16TXpRQUFBQTRBQUFBQlArTFBKZnJZOGNRQUFBUUNBQUFBQVFBRUFBa0FBQUFGYm1GdFpYTUFBQUFRQUFBQUNRQUVBQWtBQUFBRVpuSnZiUUFFQUFrQUFBQUNkRzhBQkFBSkFBQUFCM04xY0hCdmNuUUFCQUFKQUFBQUNtTnZibVpwWkdWdVkyVUFCQUFKQUFBQUNHTnZkbVZ5WVdkbEFBUUFDUUFBQUFSc2FXWjBBQVFBQ1FBQUFBVmpiM1Z1ZEFBRUFBa0FBQUFLYUhsd1pYSmZiR2xtZEFBRUFBa0FBQUFHZDJWcFoyaDBBQUFFQWdBQUFBRUFCQUFKQUFBQUNYSnZkeTV1WVcxbGN3QUFBQTBBQUFBQ2dBQUFBUC8vLy84QUFBUUNBQUFEL3dBQUFCQUFBQUFCQUFRQUNRQUFBQXBrWVhSaExtWnlZVzFsQUFBQS9nQUFCQUlBQUFBQkFBUUFDUUFBQUFkdmNIUnBiMjV6QUFBQ0V3QUFBQVVBQUFBUUFBQUFBUUFFQUFrQUFBQUJjZ0FBQUJBQUFBQUJBQVFBQ1FBQUFCQjFibTVoYldWa0xXTm9kVzVyTFRFMEFBQUFDZ0FBQUFFQUFBQUFBQUFBRFFBQUFBRUFBQUFCQUFBQURRQUFBQUVBQUFBSkFBQUVBZ0FBQlA4QUFBQVFBQUFBQlFBRUFBa0FBQUFHWlc1bmFXNWxBQVFBQ1FBQUFBVnNZV0psYkFBRUFBa0FBQUFPY205M2JtRnRaWE11Y0hKcGJuUUFCQUFKQUFBQUNuSnZkM011ZEc5MFlXd0FCQUFKQUFBQUNtTnZiSE11ZEc5MFlXd0FBQUQrQUFBQS9nPT0ifQ== -->

<div data-pagedtable="false">
  <script data-pagedtable-source type="application/json">
{"columns":[{"label":["from"],"name":[1],"type":["fctr"],"align":["left"]},{"label":["to"],"name":[2],"type":["fctr"],"align":["left"]},{"label":["support"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["confidence"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["coverage"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["lift"],"name":[6],"type":["dbl"],"align":["right"]},{"label":["count"],"name":[7],"type":["int"],"align":["right"]},{"label":["hyper_lift"],"name":[8],"type":["dbl"],"align":["right"]},{"label":["weight"],"name":[9],"type":["dbl"],"align":["right"]}],"data":[{"1":"{corn}","2":"{starch}","3":"0.01433121","4":"0.4090909","5":"0.03503185","6":"2.970047","7":"36","8":"1.8","9":"0.5877867"}],"options":{"columns":{"min":{},"max":[10],"total":[9]},"rows":{"min":[10],"max":[10],"total":[1]},"pages":{}}}
  </script>
</div>

<!-- rnb-frame-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


La confianza es considerablemente alta, aunque tenemos pocos datos de esta combinación. Podemos examinar algunos ejemplos:


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucmVjZXRhcyAlPiUga2VlcCh+IFwidG9tYXRvXCIgJWluJSAueCAmIFwiY29yblwiICVpbiUgLngpICU+JSBoZWFkKDEwKVxuYGBgIn0= -->

```r
recetas %>% keep(~ "tomato" %in% .x & "corn" %in% .x) %>% head(10)

[[1]]
 [1] "tomato"        "vinegar"       "pork"          "celery_oil"    "leek"         
 [6] "corn"          "black_pepper"  "pepper"        "ginger"        "pea"          
[11] "garlic"        "soybean"       "soy_sauce"     "chicken_broth" "wine"         

[[2]]
 [1] "tomato"     "vinegar"    "pepper"     "celery_oil" "corn"       "cayenne"   
 [7] "pork"       "garlic"     "soybean"    "vegetable"  "coriander"  "rice"      
[13] "soy_sauce" 

[[3]]
[1] "tomato"     "vinegar"    "pork"       "celery_oil" "soy_sauce"  "ginger"    
[7] "garlic"     "sherry"     "corn"      

[[4]]
 [1] "pepper"        "celery_oil"    "starch"        "corn"          "ginger"       
 [6] "garlic"        "soybean"       "tomato"        "vinegar"       "beef"         
[11] "soy_sauce"     "cayenne"       "scallion"      "bell_pepper"   "vegetable_oil"
[16] "rice"          "wine"         

[[5]]
 [1] "tomato"     "vinegar"    "pork"       "celery_oil" "beef"       "soy_sauce" 
 [7] "ginger"     "garlic"     "corn"       "wine"      

[[6]]
 [1] "tomato"      "vinegar"     "pepper"      "lemon_juice" "celery_oil"  "sake"       
 [7] "corn"        "pork"        "ginger"      "honey"       "garlic"      "soybean"    
[13] "rice"        "soy_sauce"  

[[7]]
[1] "tomato"  "garlic"  "onion"   "bacon"   "corn"    "cayenne" "egg"    

[[8]]
 [1] "pork"              "green_bell_pepper" "celery_oil"        "starch"           
 [5] "corn"              "garlic"            "tomato"            "vinegar"          
 [9] "onion"             "soy_sauce"         "cider"             "scallion"         
[13] "celery"            "pineapple"         "vegetable_oil"     "egg"              

[[9]]
 [1] "tomato"       "vinegar"      "pepper"       "celery_oil"   "roasted_pork"
 [6] "soy_sauce"    "ginger"       "honey"        "garlic"       "cinnamon"    
[11] "soybean"      "sherry"       "corn"         "oyster"      

[[10]]
 [1] "cane_molasses" "tomato"        "pork"          "celery_oil"    "vinegar"      
 [6] "soy_sauce"     "pepper"        "ginger"        "garlic"        "corn"